Cardiovasoactive Peptides in Hemodialysis Patients: Diagnostic Tools and Predictors of Outcome: A Review of Present Knowledge and Future Directions
Bibliographic record
Abstract
The main cardiovasoactive peptides involved in cardiovascular adaptation to renal failure and dialysis are reviewed with a special focus on their possible role in pathophysiology, diagnosis of cardiovascular and fluid volume abnormalities, and prognostic information. The role of vasoactive peptides in cardiovascular stability during hemodialysis (HD) are best seen in sequential HD, where the release of vasoconstrictors is stimulated by volume reduction during ultrafiltration, but is blunted during isovolemic HD, whereas plasma vasodilators increase. Plasma levels of the natriuretic peptides atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) are elevated in fluid volume overload and heart failure and decrease during dialysis. Neuropeptide Y (NPY) is elevated in severe volume overload and hypertension and calcitonin gene-related peptide in large-volume overload. Plasma BNP increases with left ventricular failure and improves during dialysis. Activation of the sympathetic nervous system as reflected by increased plasma levels of NPY is associated with poor prognosis. High levels of the natriuretic peptides ANP and BNP are likewise predictors of poor prognosis. Determinations of plasma levels of cardiovasoactive peptides may be helpful in clinical practice to diagnose volume overload and heart failure and to assess the severity of heart failure and of hypertension, as a guide to the choice of dialysis treatment and pharmacotherapy and to monitor treatment. Clinical studies will be needed in HD patients to establish the value of measurement of plasma cardiovasoactive peptides in clinical practice. The research in this field is still in its infancy and promises to be exciting in the future. There appears to be a balance of vasomotor tone and cardiac response to meet any emergency and stress such as intermittent dialysis. Further knowledge will increase our chances for major therapeutic interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".